數位素養為健康識能之預測因子

研究背景：健康識能是指個體在實現個人健康需求時，所需具備的知識與能力。過去許多研究皆指出，健康識能越高對於健康成果有正向影響；而數位素養則是指個人在獲取網路資訊後進行評斷，所需具備的技能。數位素養對於現代生活扮演著至關重要的角色，然而，研究發現，部分群體的數位素養不足，導致了所謂數位落差的發生。而在台灣，以數位素養作為健康識能預測因子的研究仍相當不足。研究目標：這項研究旨在透過台灣一般大眾的方便抽樣後，探討數位素養對於健康識能的影響。隨機抽樣後的樣本依地區被分為兩組，用以比較來自大台北地區與其他地區的數據資料，其中，影響健康識能的社會人口學變項也納入研究考量。研究方法：我們透過線上自填表搜集研究對象的經濟與社會背景、健康識能及數位素養等資料，並利用多元線性回歸模型進行分析。研究結果：從總體模型的結果顯示，健康識能總分變異數有顯著比例的差異（R^2= .36, F(9, 1215) = 72.25, p < .001）；倘若比較大台北地區與其他地區的資料，也有相似但較小比例的變異數差。以健康是能作為應變數的分析中，大台北地區的整體模型配適度為R^2= = .29 F(9, 593) = 26.91 p < .001；而其他地區則是R^2= = .45 F(9, 1215) = 56.25 p < .001。在總體樣本中，數位素養可顯著預測健康識能的表現（β= –.56, t(1215) = 23.78, p < .001）。對於兩個子樣本中的各分量表中，顯示數位素養與各指標相比，皆有較高的標準化係數，利用BRIEF模型分析後，也有相同結果。結論：本結果反映了數位素養很可能與健康識能有極高的關聯性，說明縮小數位落差對於改善群眾健康的重要性，可作為未來學界進行因果關係檢定的數據參考。

關鍵字

健康識能；數位素養；數位落差分析；電子健康識能；健康之社會決定因素

並列摘要

Background: Health literacy (HL) consists of the knowledge and competencies needed by an individual to fulfill their health needs. Research has consistently shown health literacy to be an indicator of positive health outcomes. Digital Literacy (DL) consists of the skills needed to obtain and evaluate information on the internet. DL is crucial for many aspects of modern life, yet low DL in certain populations has been observed and this phenomenon is referred to as the digital divide. There is a lack of research in Taiwan examining digital literacy as a predictor of health literacy. Objective: This study aimed to examine the effect of digital literacy on health literacy among a sample of Taiwan’s general population. The sample was further split into two sub-samples, representing those from Greater Taipei and those from outside Greater Taipei. Demographic characteristics shown to influence health literacy were also examined in this sample. Methods: Self-reported health literacy, digital literacy, and demographic characteristics were collected from online questionnaires using convenience sampling. The results were analysed using multiple regression. Results: The overall model explained a significant proportion of variance in comprehensive HL scores, R2 = .36, F(9, 1215) = 72.25, p < .001. Similar results were observed using Greater Taipei and in the Rest of Taiwan, but the model explained a smaller proportion of the variance with the Greater Taipei sub-sample. The Greater Taipei area’s overall model fit with comprehensive health literacy as the dependent variable was observed to be R^2= = .29 F(9, 593) = 26.91 p < .001, while for the rest of Taiwan the model fit was R^2= = .45 F(9, 1215) = 56.25 p < .001. In the full sample, digital literacy significantly predicted comprehensive health literacy, β= –.56, t(1215) = 23.78, p < .001. For every measure and each subscale and in both sub-samples, digital literacy consistently showed higher standardized coefficients than any covariates. The result was also observed in the regression using the BRIEF. Conclusions: This study seems to indicate that digital literacy is a strong predictor of health literacy. Further research should be undertaken to determine the exact causal nature of this relationship. This study underscores the importance that closing the digital divide has for health.

並列關鍵字

Health Literacy ； Digital Literacy ； Digital Divide ； eHealth Literacy ； Social Determinants of Health

參考文獻

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數位素養為健康識能之預測因子

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